Instead of an abrupt prediction of the last layer, the hourglass approach [1]}, [2]}, [3]}, [4]} (i.e., models with encoder-decoder stages, such as U-Net [5]}, DeconvNet [6]}, SegNet [7]}) created a decoder stage to gradually recover the spatial information by combining multi-level feature maps from the encoder.
Thus, the flow of information from a lower scale to a higher one is done by an upsampling operation, i.e., bilinear interpolation [8]}, unpooling [6]}, or DUpsampling [10]}.
We consider hourglass models to have a robust decoding stage for the reconstruction of the pixel-wise predicted image.
Although hourglass models proved efficient in SS, they still need a more significant transfer of information between its stages, e.g., FC-DenseNet [11]} or UPSNet [12]}.
For this reason, we add complementary information to the models by adding the MTL approach (i.e., auxiliary task).
